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PloS One 2015Diagnosing obstructive sleep apnea (OSA) is clinically relevant because untreated OSA has been associated with increased morbidity and mortality. The STOP-Bang... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Diagnosing obstructive sleep apnea (OSA) is clinically relevant because untreated OSA has been associated with increased morbidity and mortality. The STOP-Bang questionnaire is a validated screening tool for OSA. We conducted a systematic review and meta-analysis to determine the effectiveness of STOP-Bang for screening patients suspected of having OSA and to predict its accuracy in determining the severity of OSA in the different populations.
METHODS
A search of the literature databases was performed. Inclusion criteria were: 1) Studies that used STOP-Bang questionnaire as a screening tool for OSA in adult subjects (>18 years); 2) The accuracy of the STOP-Bang questionnaire was validated by polysomnography--the gold standard for diagnosing OSA; 3) OSA was clearly defined as apnea/hypopnea index (AHI) or respiratory disturbance index (RDI) ≥ 5; 4) Publications in the English language. The quality of the studies were explicitly described and coded according to the Cochrane Methods group on the screening and diagnostic tests.
RESULTS
Seventeen studies including 9,206 patients met criteria for the systematic review. In the sleep clinic population, the sensitivity was 90%, 94% and 96% to detect any OSA (AHI ≥ 5), moderate-to-severe OSA (AHI ≥15), and severe OSA (AHI ≥30) respectively. The corresponding NPV was 46%, 75% and 90%. A similar trend was found in the surgical population. In the sleep clinic population, the probability of severe OSA with a STOP-Bang score of 3 was 25%. With a stepwise increase of the STOP-Bang score to 4, 5, 6 and 7/8, the probability rose proportionally to 35%, 45%, 55% and 75%, respectively. In the surgical population, the probability of severe OSA with a STOP-Bang score of 3 was 15%. With a stepwise increase of the STOP-Bang score to 4, 5, 6 and 7/8, the probability increased to 25%, 35%, 45% and 65%, respectively.
CONCLUSION
This meta-analysis confirms the high performance of the STOP-Bang questionnaire in the sleep clinic and surgical population for screening of OSA. The higher the STOP-Bang score, the greater is the probability of moderate-to-severe OSA.
Topics: Humans; Mass Screening; Polysomnography; Reproducibility of Results; Severity of Illness Index; Sleep Apnea, Obstructive; Surveys and Questionnaires
PubMed: 26658438
DOI: 10.1371/journal.pone.0143697 -
Health Technology Assessment... Oct 2019Osteomyelitis is an infection of the bone. Medical imaging tests, such as radiography, ultrasound, magnetic resonance imaging (MRI), single-photon emission computed...
BACKGROUND
Osteomyelitis is an infection of the bone. Medical imaging tests, such as radiography, ultrasound, magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and positron emission tomography (PET), are often used to diagnose osteomyelitis.
OBJECTIVES
To systematically review the evidence on the diagnostic accuracy, inter-rater reliability and implementation of imaging tests to diagnose osteomyelitis.
DATA SOURCES
We conducted a systematic review of imaging tests to diagnose osteomyelitis. We searched MEDLINE and other databases from inception to July 2018.
REVIEW METHODS
Risk of bias was assessed with QUADAS-2 [quality assessment of diagnostic accuracy studies (version 2)]. Diagnostic accuracy was assessed using bivariate regression models. Imaging tests were compared. Subgroup analyses were performed based on the location and nature of the suspected osteomyelitis. Studies of children, inter-rater reliability and implementation outcomes were synthesised narratively.
RESULTS
Eighty-one studies were included (diagnostic accuracy: 77 studies; inter-rater reliability: 11 studies; implementation: one study; some studies were included in two reviews). One-quarter of diagnostic accuracy studies were rated as being at a high risk of bias. In adults, MRI had high diagnostic accuracy [95.6% sensitivity, 95% confidence interval (CI) 92.4% to 97.5%; 80.7% specificity, 95% CI 70.8% to 87.8%]. PET also had high accuracy (85.1% sensitivity, 95% CI 71.5% to 92.9%; 92.8% specificity, 95% CI 83.0% to 97.1%), as did SPECT (95.1% sensitivity, 95% CI 87.8% to 98.1%; 82.0% specificity, 95% CI 61.5% to 92.8%). There was similar diagnostic performance with MRI, PET and SPECT. Scintigraphy (83.6% sensitivity, 95% CI 71.8% to 91.1%; 70.6% specificity, 57.7% to 80.8%), computed tomography (69.7% sensitivity, 95% CI 40.1% to 88.7%; 90.2% specificity, 95% CI 57.6% to 98.4%) and radiography (70.4% sensitivity, 95% CI 61.6% to 77.8%; 81.5% specificity, 95% CI 69.6% to 89.5%) all had generally inferior diagnostic accuracy. Technetium-99m hexamethylpropyleneamine oxime white blood cell scintigraphy (87.3% sensitivity, 95% CI 75.1% to 94.0%; 94.7% specificity, 95% CI 84.9% to 98.3%) had higher diagnostic accuracy, similar to that of PET or MRI. There was no evidence that diagnostic accuracy varied by scan location or cause of osteomyelitis, although data on many scan locations were limited. Diagnostic accuracy in diabetic foot patients was similar to the overall results. Only three studies in children were identified; results were too limited to draw any conclusions. Eleven studies evaluated inter-rater reliability. MRI had acceptable inter-rater reliability. We found only one study on test implementation and no evidence on patient preferences or cost-effectiveness of imaging tests for osteomyelitis.
LIMITATIONS
Most studies included < 50 participants and were poorly reported. There was limited evidence for children, ultrasonography and on clinical factors other than diagnostic accuracy.
CONCLUSIONS
Osteomyelitis is reliably diagnosed by MRI, PET and SPECT. No clear reason to prefer one test over the other in terms of diagnostic accuracy was identified. The wider availability of MRI machines, and the fact that MRI does not expose patients to harmful ionising radiation, may mean that MRI is preferable in most cases. Diagnostic accuracy does not appear to vary with the potential cause of osteomyelitis or with the body part scanned. Considerable uncertainty remains over the diagnostic accuracy of imaging tests in children. Studies of diagnostic accuracy in children, particularly using MRI and ultrasound, are needed.
STUDY REGISTRATION
This study is registered as PROSPERO CRD42017068511.
FUNDING
This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full in ; Vol. 23, No. 61. See the NIHR Journals Library website for further project information.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Child; Child, Preschool; Cost-Benefit Analysis; Female; Humans; Infant; Magnetic Resonance Imaging; Male; Middle Aged; Osteomyelitis; Positron-Emission Tomography; Reproducibility of Results; Technology Assessment, Biomedical; Ultrasonography; Young Adult
PubMed: 31670644
DOI: 10.3310/hta23610 -
Medicina (Kaunas, Lithuania) Sep 2021: Anatomical post-surgical alterations of the upper gastrointestinal (GI) tract have always been challenging for performing diagnostic and therapeutic endoscopy,... (Review)
Review
: Anatomical post-surgical alterations of the upper gastrointestinal (GI) tract have always been challenging for performing diagnostic and therapeutic endoscopy, especially when biliopancreatic diseases are involved. Esophagectomy, gastrectomy with various reconstructions and pancreaticoduodenectomy are among the most common surgeries causing upper GI tract alterations. Technological improvements and new methods have increased the endoscopic success rate in these patients, and the literature has been rapidly increasing over the past few years. The aim of this systematic review is to identify evidence on the available biliopancreatic endoscopic techniques performed in the altered post-surgical anatomy of upper GI tract. : We performed a systematic search of PubMed, MEDLINE, Cochrane Library, and SCOPUS databases. Study-level variables extracted were the last name of the first author, publication year, study design, number of patients, type of post-surgical anatomical alteration, endoscopic technique, success rate and endoscopic-related adverse events. : Our primary search identified 221 titles, which was expanded with studies after the citation search. The final full-text review process identified 52 articles (31 retrospective studies, 8 prospective studies and 13 case reports). We found several different techniques developed over the years for biliopancreatic diseases in altered anatomy, in order to perform both endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP). They included enteroscopy-assisted ERCP (double and single balloon enteroscopy-ERCP, spiral enteroscopy-ERCP) laparoscopic assisted ERCP, EUS-Directed transgastric ERCP, EUS-directed transgastric intervention, gastric access temporary for endoscopy, and percutaneous assisted trans prosthetic endoscopic therapy. The success rate was high (most of the techniques showed a success rate over 90%) and a low rate of adverse events were reported. : We suggest the considerationof the novel techniques when approaching patients with altered anatomy who require biliopancreatic endoscopy, focusing on the surgery type, success rate and adverse events reported in the literature.
Topics: Cholangiopancreatography, Endoscopic Retrograde; Endosonography; Gastrectomy; Humans; Prospective Studies; Retrospective Studies
PubMed: 34684051
DOI: 10.3390/medicina57101014 -
World Journal of Emergency Surgery :... Dec 2023To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional... (Review)
Review
BACKGROUND
To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes.
MAIN BODY
A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.
RESULTS
In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.
CONCLUSION
AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.
Topics: Adult; Humans; Artificial Intelligence; Appendicitis; Prognosis; Algorithms; Machine Learning; Acute Disease
PubMed: 38114983
DOI: 10.1186/s13017-023-00527-2 -
International Journal of Environmental... May 2022Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a... (Review)
Review
Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
Topics: Artificial Intelligence; Computers; Diagnosis, Computer-Assisted; Humans; Intervertebral Disc Degeneration; Low Back Pain
PubMed: 35627508
DOI: 10.3390/ijerph19105971 -
Physical and Engineering Sciences in... Mar 2022To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of... (Review)
Review
OBJECTIVES
To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work.
METHODS
The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test.
FINDINGS
Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified.
INTERPRETATION
A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
Topics: Artificial Intelligence; COVID-19; COVID-19 Testing; Humans; Publishing; Radiography; SARS-CoV-2
PubMed: 34919204
DOI: 10.1007/s13246-021-01093-0 -
Clinics (Sao Paulo, Brazil) 2024Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder, with main manifestations related to communication, social interaction, and behavioral... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder, with main manifestations related to communication, social interaction, and behavioral patterns. The slight dynamics of change in the child over time require that the onset of clinical manifestations presented by the child be more valued, with the aim of stabilizing the condition. Faced with a variety of methods for diagnosing ASD, the question arises as to which method should be used. This systematic review aims to recommend the best tools to perform screening and diagnosis.
METHODOLOGY
This systematic review followed the PRISMA guidelines. The databases MEDLINE, Embase, CENTRAL (Cochrane), and Lilacs were accessed, and gray and manual searches were performed. The search strategy was created with terms referring to autism and the diagnosis/broad filter. The studies were qualitatively evaluated and quantitatively. Statistical analysis was performed using Meta-diSc-2.0 software, the confidence interval was 95 %.
RESULTS
The M-CHAT-R/F tool demonstrated a sensitivity of 78 % (95 % CI 0.57‒0.91) and specificity of 0.98 (95 % CI 0.88-1.00). The diagnostic tools demonstrated sensitivity and specificity respectively of: ADOS, sensitivity of 87 % (95 % CI 0.79‒0.92) and specificity 75 % (95 % CI 0.73‒0.78); ADI-R demonstrated test sensitivity of 77 % (95 % CI 0.56‒0.90) and specificity 68 % (95 % CI 0.52‒0.81), CARS test sensitivity was 89 % (95 % CI 0.78‒0.95) and specificity 79 % (95 % CI 0.65‒0.88).
CONCLUSION
It is mandatory to apply a screening test, the most recommended being the M-CHAT-R/F. For diagnosis CARS and ADOS are the most recommended tools.
Topics: Child; Humans; Autism Spectrum Disorder; Sensitivity and Specificity; Mass Screening; Communication; Research Design
PubMed: 38484581
DOI: 10.1016/j.clinsp.2023.100323 -
Medicina (Kaunas, Lithuania) May 2023The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent... (Review)
Review
The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.
Topics: Animals; Humans; Child; Artificial Intelligence; Liver Cirrhosis; Biopsy; Databases, Factual; Inflammation
PubMed: 37241224
DOI: 10.3390/medicina59050992 -
Medicina (Kaunas, Lithuania) Oct 2022: The positive implications of using free light chains in diagnosing multiple sclerosis have increasingly gained considerable interest in medical research and the... (Meta-Analysis)
Meta-Analysis Review
: The positive implications of using free light chains in diagnosing multiple sclerosis have increasingly gained considerable interest in medical research and the scientific community. It is often presumed that free light chains, particularly kappa and lambda free light chains, are of practical use and are associated with a higher probability of obtaining positive results compared to oligoclonal bands. The primary purpose of the current paper was to conduct a systematic review to assess the up-to-date methods for diagnosing multiple sclerosis using kappa and lambda free light chains. : An organized literature search was performed across four electronic sources, including Google Scholar, Web of Science, Embase, and MEDLINE. The sources analyzed in this systematic review and meta-analysis comprise randomized clinical trials, prospective cohort studies, retrospective studies, controlled clinical trials, and systematic reviews. : The review contains 116 reports that includes 1204 participants. The final selection includes a vast array of preexisting literature concerning the study topic: 35 randomized clinical trials, 21 prospective cohort studies, 19 retrospective studies, 22 controlled clinical trials, and 13 systematic reviews. : The incorporated literature sources provided integral insights into the benefits of free light chain diagnostics for multiple sclerosis. It was also evident that the use of free light chains in the diagnosis of clinically isolated syndrome (CIS) and multiple sclerosis is relatively fast and inexpensive in comparison to other conventional state-of-the-art diagnostic methods, e.g., using oligoclonal bands (OCBs).
Topics: Humans; Oligoclonal Bands; Multiple Sclerosis; Retrospective Studies; Prospective Studies; Immunoglobulin kappa-Chains; Immunoglobulin lambda-Chains; Immunoglobulin Light Chains; Randomized Controlled Trials as Topic
PubMed: 36363469
DOI: 10.3390/medicina58111512 -
Journal of Obstetrics and Gynaecology :... Dec 2024The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain. Imaging techniques are evolving constantly. This study aimed to systematically assess the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) in detecting endometrioma using the surgical visualisation of lesions with or without histopathological confirmation as reference standards in patients of reproductive age with suspected endometriosis.
METHODS
PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature and ClinicalTrials.gov databases were searched from their inception to 12 October 2022, using a manual search for additional articles. Two authors independently performed title, abstract and full-text screening of the identified records, extracted study details and quantitative data and assessed the quality of the studies using the 'Quality Assessment of Diagnostic Accuracy Study 2' tool. Bivariate random-effects models were used to determine the pooled sensitivity and specificity, compare the two imaging modalities and evaluate the sources of heterogeneity.
RESULTS
Sixteen prospective studies (10 assessing TVUS, 4 assessing MRI and 2 assessing both TVUS and MRI) were included, representing 1976 participants. Pooled TVUS and MRI sensitivities for endometrioma were 0.89 (95% confidence interval 'CI', 0.86-0.92) and 0.94 (95% CI, 0.74-0.99), respectively (indirect comparison -value of 0.47). Pooled TVUS and MRI specificities for endometrioma were 0.95 (95% CI, 0.92-0.97) and 0.94 (95% CI, 0.89-0.97), respectively (indirect comparison p-value of 0.51). These studies had a high or unclear risk of bias. A direct comparison (all participants undergoing TVUS and MRI) of the modalities was available in only two studies.
CONCLUSION
TVUS and MRI have high accuracy for diagnosing endometriomas; however, high-quality studies comparing the two modalities are lacking.
Topics: Female; Humans; Endometriosis; Prospective Studies; Ultrasonography; Magnetic Resonance Imaging; Sensitivity and Specificity; Diagnostic Tests, Routine
PubMed: 38348799
DOI: 10.1080/01443615.2024.2311664